Project Summary. This project is designed to identify validated biomarkers for use in the assessment of electronic nicotine delivery systems (ENDS) by the FDA. Since the introduction of ENDS, commonly referred to as e-cigarettes, there has been a large increase in ENDS use among young adults and older traditional cigarette smokers who also use ENDS (dual users). Since 2016, the Food and Drug Administration (FDA) has had regulatory authority over ENDS, and there is an acute need for ENDS-related biomarkers that can be used as validated surrogate endpoints for evaluation of new ENDS products. With the goal of validated biomarker discovery in two independent cohorts, the COPDGene and UCSD ENDS studies, we propose to identify ENDS-related inflammatory biomarkers in ENDS only and dual users and relate these biomarkers to five-year lung health outcomes. COPDGene is an ongoing, longitudinal study of >6,000 current and former traditional cigarette (t-cig) smokers enriched for chronic obstructive pulmonary disease (COPD) with detailed longitudinal lung phenotyping data (including chest CT), genome-wide blood RNA-seq, and proteomic data. The UCSD ENDS Study is a controlled study of young ENDS only users and controls with detailed assessment of inflammatory biomarkers in the oropharynx, airways and blood. Biomarkers used as validated surrogate measures must be 1) associated with ENDS use, 2) predictive of health outcomes, and 3) have a strong biological rationale. We hypothesize that inflammatory biomarkers of ENDS use will be predictive of five-year lung health effects. In Aim 1 of this proposal, discovery of inflammatory transcriptomic and proteomic biomarkers of ENDS exposure will be performed in subjects from the COPDGene five-year study visit, and biomarkers will be validated in two independent sets of subjects from the COPDGene ten-year visit and the UCSD ENDS Study. In Aim 2 we will identify antibody-specific adaptive immune response biomarkers of ENDS exposure using adaptive immune receptor repertoire sequencing (AIRR-seq). Auto-antibodies are biomarkers that are associated with the degree of lung damage in COPD. AIRR-seq is a powerful tool for inflammatory biomarker discovery that characterizes an individual?s decades-long history of antibody responses. In Aim 3 we will use machine learning predictive models to relate ENDS-associated biomarker panels to five-year lung health outcomes from COPDGene. The investigative team for this grant is well-positioned to identify novel inflammatory biomarkers of ENDS use. The COPDGene and UCSD cohorts have the detailed lung phenotyping and molecular characterization necessary to discover and clinically validate biomarkers in two important populations of ENDS users, i.e. ENDS only and dual users.